Mind the Gap: Addressing "Missing Pieces" in Neuroscience Curricula to Reinforce Research Rigor

注意差距：解决神经科学课程中的“缺失部分”以加强研究严谨性

• 批准号: 10668514
• 负责人: RICHARD T BORN
• 金额: $ 8.53万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10668514
• 关键词: Active Learning; Address; Area; Awareness; Basic Science; Biological; Clinical; Code; Cognitive; Communities; Comprehension; Data; Data Analyses; Development; Doctor of Philosophy; Documentation; Education; Educational Curriculum; Educational process of instructing; Educational workshop; Elements; Ensure; Exercise; Experimental Designs; Faculty; Failure; Fostering; Glossary; Health; Human; Instruction; Interview; Knowledge; Learning; Mathematics; Methods; Mind; Morphologic artifacts; Names; Neurosciences; Neurosciences Research; Outcome; Output; Postdoctoral Fellow; Readability; Reading; Recording of previous events; Reproducibility; Research; Research Personnel; Research Project Grants; Resources; Sampling; Scientist; Source; Students; System; Teaching Materials; Testing; Thinking; Time; Training; Training Programs; Universities; Visualization; Writing; analysis pipeline; animation; combat; computational neuroscience; computerized data processing; design; experimental analysis; experimental study; graduate student; improved; innovation; instructor; pedagogical content; programs; sound; student participation; tool; translational pipeline; undergraduate student

ABSTRACT The PhD Program in Neuroscience at Harvard University has been a leader in the development of innovative curricula for training in research rigor, beginning with the creation, in June 2011, of the course "Quantitative Methods for Biologists," led by Drs. Michael Springer and Richard Born. Since this time, we have continued to develop curricula in this area, including specific instruction in research rigor and reproducibility from a statistical perspective in the course "Thinking about data," taught by Dr. Born. We propose to extend and enhance these efforts by developing teaching materials that will address "missing pieces" in neuroscience curricula to reinforce research rigor: 1) identifying and eliminating sources of bias in experimental design and analysis, 2) distinguishing experimental failures from biological answers; 3) investigating the details and assumptions of analyses and 4) making coded analyses reproducible. Our pedagogical approach will emphasize active learning and embedding concepts within a specific neuroscience context to enhance student engagement, learning, and retention. All units will feature interviews with diverse scientists to highlight the importance of the topic and tutorials of varying difficulty focused on specific neuroscience examples. These tutorials will consist of short animated videos to introduce key ideas, interactive visualizations, discussion questions, real data when appropriate, and optional coding exercises, and will be available in various levels of difficulty and assumed expertise. All units will include a “learner resources page” with summary notes, a glossary, and pointers to further resources on the topic, as well as an instructor resources page with sample lesson plans, assessment question banks, and downloadable content.

摘要 哈佛大学的神经科学博士项目在发展 创新的研究严谨培训课程，从2011年6月创建 课程“生物学家的定量方法”，由迈克尔·斯普林格博士和理查德·伯恩博士领导。既然是这样 一段时间以来，我们继续开发这一领域的课程，包括在研究严谨性方面的具体指导 在伯恩博士讲授的“思考数据”课程中，从统计学的角度研究数据的可重复性。 我们建议通过开发教材来扩大和加强这些努力， 神经科学课程中加强研究严谨性的“遗漏片段”：1)识别和消除 实验设计和分析中的偏差来源，2)区分实验失败和 生物学答案；3)调查分析的细节和假设；4)进行编码 分析是可重复的。我们的教学方法将强调主动学习和嵌入 在特定神经科学背景下的概念，以提高学生的参与度、学习和 留存。所有单元都将与不同的科学家进行访谈，以突出这一主题的重要性 不同难度的教程集中在具体的神经科学例子上。这些教程将包括 介绍关键思想、交互式可视化、讨论问题的动画短片 适当时的数据和可选的编码练习，并将在不同的难度级别提供 并自认为有专业知识。所有单元都将包括一个带有总结笔记的“学习者资源页面”， 词汇表和指向该主题的更多资源的指针，以及讲师资源页面 样例课程计划、评估题库和可下载内容。